Triple
T33869709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Attica Prison uprising |
E868165
|
entity |
| Predicate | otherFatalities |
P1785
|
FINISHED |
| Object | 4 (including prison staff and inmates before the final assault) |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 4 (including prison staff and inmates before the final assault) | Statement: [Attica Prison uprising, otherFatalities, 4 (including prison staff and inmates before the final assault)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherFatalities Context triple: [Attica Prison uprising, otherFatalities, 4 (including prison staff and inmates before the final assault)]
-
A.
causedFatalities
Indicates that the referenced event or action directly resulted in one or more deaths.
-
B.
deathToll
chosen
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
C.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
D.
otherMajorTragedy
Indicates that the subject experienced or was involved in a significant tragic event other than the primary or most notable tragedy under consideration.
-
E.
killedManyVictims
Indicates that an entity has killed a large number of distinct victims.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f34995029081909ede0f7df73d1a5e |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f73223675481908c1bc3208c0f5284 |
completed | May 3, 2026, 11:31 a.m. |
| PD | Predicate disambiguation | batch_69f7317690108190b3aae2cd2e1d069e |
completed | May 3, 2026, 11:28 a.m. |
Created at: May 1, 2026, 1:47 a.m.